International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Extracting Shape Features in JPEG-2000 Compressed Images
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Subband-Based, Memory-Efficient JPEG2000 Images Indexing in Compressed-Domain
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
JPEG2000 and Motion JPEG2000 Content Analysis Using Codestream Length Information
DCC '05 Proceedings of the Data Compression Conference
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Format-compliant jpeg2000 encryption with combined packet header and packet body protection
Proceedings of the 9th workshop on Multimedia & security
EURASIP Journal on Information Security
Key-dependent JPEG2000-based robust hashing for secure image authentication
EURASIP Journal on Information Security
Adaptive directional wavelet transform based on directional prefiltering
IEEE Transactions on Image Processing
Coarse to fine K nearest neighbor classifier
Pattern Recognition Letters
Texture-based medical image retrieval in compressed domain using compressive sensing
International Journal of Bioinformatics Research and Applications
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In many applications, the amount and resolution of digital images have significantly increased over the past few years. For this reason, there is a growing interest for techniques allowing to efficiently browse and seek information inside such huge data spaces. JPEG 2000, the latest compression standard from the JPEG committee, has several interesting features to handle very large images. In this paper, these features are used in a coarse-to-fine approach to retrieve specific information in a JPEG 2000 code-stream while minimizing the computational load required by such processing. Practically, a cascade of classifiers exploits the bit-depth and resolution scalability features intrinsically present in JPEG 2000 to progressively refine the classification process. Comparison with existing techniques is made in a texture-retrieval task and shows the efficiency of such approach.